Semantic networks are often used as forms of knowledge representation. A semantic network is a directed graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between the concepts.
In contrast to a network object, a networking activity comprises the active creation and reorganization of networks. Social networking is one example of a networking activity, wherein a network of acquaintances is built and revised. Members can use such a network to facilitate active interactions with other members. These networking interactions can result in changes to the network.
Whereas semantic networks are directed graphs, semantic networking is the process of developing those graphs.
It has generally been a challenge to those skilled in the art to create representations of abstract knowledge for mass market, consumer-focused activities. One reason for this is that the subject matter of a semantic network in consumer markets is often highly subjective and personal. Unlike organizational settings, it cannot be prescribed or given a universal representation for individual consumers.
In addition, knowledge entities are multifaceted; that is, they can have many different organizational bases (or dimensions). As the number of dimensions increases beyond three or four, representations of multidimensional objects, matrices, or networks quickly become too unwieldy for the cognitive abilities of human users. This problem is compounded when combining the representations of one person with those of others, because each person will have his or her own unique perspectives and vocabularies.
Another difficulty is that of scalability: thoughts and knowledge are unbounded, and representing them poses problems of storage and management.
Furthermore, semantic networking requires a process to guide it. Cognitive agents such as people may direct the production of knowledge representations, but the process must be mediated to be effective. Current approaches of doing so include: ontology-building and taxonomy-building tools; semantic web and collective knowledge initiatives; folksonomies (such as social tagging); semantic extraction (from legacy representations of knowledge); data mining; and others. These approaches require users to modify or accommodate their thinking in support of the technology. To be more effective, the system design should be directed by how people think.
Clearly, semantic networking processes are quite different than, for example, the general notion of semantic networks, which as previously mentioned are merely representations of a state of knowledge. Semantic networks have a broad utility as a form of knowledge representation. As machine-readable data, they can support a number of advanced technologies, such as artificial intelligence, software automation and “agents”, expert systems, and knowledge management. Additionally, they can be transformed into various forms of media (other knowledge representations). In other words, the synthesis or creation of semantic networks can support the synthesis of a broad swath of media to extract additional value from the semantic network.
Some approaches to synthesize media are presently known. For example, NLP/grammar-based/linguistic document structure analysis is utilized as a lattice for collating content components and deducting component linking and alignment to form synthesized media. Additionally, multi-document summarization method is known, whereby common and diverse elements are captured across a number of documents, and merges or organizes these under a common these. Another approach involves utilizing a single super-document (e.g. a content model) and applying document transformations, for example, such as XSLT or XSD, to synthesize smaller subsets of documents. Still another known approach is synthesis in response to a complex specification from a user whereby user requirements are modelled, either implicitly, for example, such as demographic profiles, or explicitly, for example, such as by specific topics or perspectives, to provide a basis for synthesis operations. Other known approaches include social/collaborative/Web 2.0.
The above-listed methods do not provide options for consumers of media to direct the synthesis process. Consumers requiring personally tailored media must either create the document from scratch or use synthesizing approaches that are based upon existing documents or sources.